This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
zinfradb
Database for AI apps
zinfradb is a single-process database for AI apps: vector, graph, lexical, and hybrid search with durable jobs in one C++20 binary. Built for RAG, agent memory, and retrieval-shaped products. Fuses vector similarity, BM25 lexical, and graph evidence in one query with caller-tunable weights and per-result explain. Top of the ANN benchmark board at recall ≥ 0.95. One docker run, admin UI included. Source-available under BSL 1.1.
Building with AI, I kept hitting the same wall: models forget everything the moment a conversation ends. The usual fix — bolt on a vector database — papers over it, but a bag of embeddings isn't really memory. It finds text similar to a query, yet has no idea where a fact came from, what it contradicts, or what changed last week. I wanted memory that understands how knowledge connects, not just how it clusters. So vector, full-text, and graph live in one engine.
About zinfradb on Product Hunt
“Database for AI apps”
zinfradb was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #20 on the daily leaderboard. zinfradb is a single-process database for AI apps: vector, graph, lexical, and hybrid search with durable jobs in one C++20 binary. Built for RAG, agent memory, and retrieval-shaped products. Fuses vector similarity, BM25 lexical, and graph evidence in one query with caller-tunable weights and per-result explain. Top of the ANN benchmark board at recall ≥ 0.95. One docker run, admin UI included. Source-available under BSL 1.1.
On the analytics side, zinfradb competes within Developer Tools, Artificial Intelligence, GitHub and Database — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how zinfradb performed against the three products that launched closest to it on the same day.
Who hunted zinfradb?
zinfradb was hunted by Zishan Neno. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of zinfradb including community comment highlights and product details, visit the product overview.
Zishan Neno